What is research data management (RDM)?

The new article series brings research data management (RDM) to its basics. The opening part of the series provides an overview of what RDM is, why it concerns all researchers and how the RDM life cycle relates to the research life cycle.

(Tämä artikkeli on saatavilla myös suomeksi.)

Text: Mikko Ojanen, Tanja Lindholm & Liisa Siipilehto

Research data management (RDM) has gradually been identified as a set of tasks separate from a scientific conduct of a research project. RDM is mechanical, managerial and technical handling of research data, whereas the core content of a research project involves methodological or analytical processing of data.

Tasks related to RDM are not exactly new, however, they have not previously been considered as significant separate processes. Researchers typically have a good command of research lifecycle tasks and their management is well covered in higher education. Processes related to a RDM life cycle have remained more or less hidden. Yet the RDM tasks are the oil that keeps the wheels of research life cycle running (see Figure 1).

Figure 1. Research Life Cycle and Data Management Life Cycle as separate but synchronous processes. CC-BY UH Data Support.

RDM is about taking proper care of data and it involves all aspects of data management life cycle from planning data management to archiving data. Pay a special attention that the planning of research data management (DMP) is an integral part of ever evolving RDM. Therefore the changes in RDM processes have an effect on a project DMP, which should frequently be updated.

RDM skills are basic researcher skills, which are required from everyone handling research data in a project. Gradually, along with the rising acknowledgement of RDM as a separate process, the need for new expertise and even a new profession has emerged. Learning the RDM skills and integrating them in a project workflow is a slow process – at first. However, once the skills are routinized and the tools are adopted, RDM becomes tacit knowledge, researcher’s every day know-how, which can be applied from one project to another.

Learning the RDM skills and integrating them in a project workflow is a slow process – at first. However, once the skills are routinized and the tools are adopted, RDM becomes tacit knowledge, researcher’s every day know-how, which can be applied from one project to another.

Paying attention to the RDM components and learning how to avoid RDM hiccups will save a researcher significant amount of resources (both time and money) and ensure the effective conduct of a project. Mastering the RDM life cycle and its components help to keep the research life cycle running smoothly. When the components in the RDM life cycle do not run smoothly, the research project can be severely hindered or even halted.

In the next part of this article series, we will take a closer look at the different components of RDM.

  Research Data Management – know your data!

Research data management (RDM) is a crucial part of any research. First and foremost the aim of RDM is to make the research process as efficient as possible – secondly, it will help you to meet the expectations and requirements of your organization and research funders. RDM skills are basic researchers skills and they apply to everyone who handles research data in a research project. By learning RDM you get to KNOW YOUR DATA!

In this series, the University of Helsinki Data Support team introduces all key components related to RDM and DMP; what they are, why they are important and where to look for further help in RDMP issues. The series comprises six parts:

1) What is research data management (RDM)? (3.9.2020)
2) The components of research data management (17.9.2020)
3) Why research data management? (30.9.2020)
4) Why plan research data management in advance?
5) How to manage (your) research data?
6) Where is the help and support for research data management?

 

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